Quantifying the nonlinear relationship between block morphology and the surrounding thermal environment using random forest method

•The nonlinear effects of block morphology on the diurnal thermal environment were explored.•A machine learning RF was applied to better model the block-level thermal environment.•Different contributions of morphological variables to diurnal thermal environment were compared.•Suggestions on urban heat mitigating were proposed for decision-makers and urban planners.